def test():
opt.device = 'cuda:0'
opt.data_root = 'demo/input/' # The location of your testing data
opt.mask_root = 'demo/mask/' # The location of your testing data mask
testset = MyDataLoader(opt)
print('Test with %d' % (len(testset)))
model = MyModel()
model.initialize(opt)
model.load_networks('places_irregular') # For irregular mask inpainting
# model.load_networks('celebahq_center') # For centering mask inpainting, i.e., 120*120 hole in 256*256 input
val_ssim, val_psnr, val_mae, val_losses_G = [], [], [], []
with torch.no_grad():
for i, data in enumerate(testset):
fname = data['fname'][0]
model.set_input(data)
I_g, I_o, val_loss_G = model.optimize_parameters(val=True)
val_s, val_p, val_m = metrics(I_g, I_o)
val_ssim.append(val_s)
val_psnr.append(val_p)
val_mae.append(val_m)
val_losses_G.append(val_loss_G.detach().item())
cv2.imwrite('demo/output/' + fname[:-4] + '.png', postprocess(I_o).numpy()[0])
print('Val (%d/%d) G:%5.4f, S:%4.4f, P:%4.2f, M:%4.4f' % (
i + 1, len(testset), np.mean(val_losses_G), np.mean(val_ssim), np.mean(val_psnr), np.mean(val_mae)), end='\r')
print('Val G:%5.4f, S:%4.4f, P:%4.2f, M:%4.4f' %
(np.mean(val_losses_G), np.mean(val_ssim), np.mean(val_psnr), np.mean(val_mae)))
def test():
opt.device = 'cuda:0'
result_dir = 'results'
if not os.path.exists(result_dir): os.mkdir(result_dir)
from dataset_tfrecord import define_dataset
tfrecord_path = "/content/generator_layers_v2.1_categories.record"
batch_size = 1
testset, testset_length = define_dataset(tfrecord_path,
batch_size,
train=False,
test=True)
dpp = Preprocess() # data pre-process (dpp)
print('Test with %d' % (testset_length))
model = MyModel()
model.initialize(opt)
model.load_networks(str(38)) # For irregular mask inpainting
val_ssim, val_psnr, val_mae, val_losses_G = [], [], [], []
ids = []
test_iterator = iter(testset)
num_iterations = int(testset_length / batch_size)
def tensor2array(xx):
xx = xx.detach().cpu()[0]
xx = xx.permute(1, 2, 0).numpy()
xx = (xx + 1) * 0.5
xx = np.clip(xx, 0, 1)
if xx.shape[2] == 1:
return np.concatenate([xx, xx, xx], -1)[:, 32:32 + 192, :]
if xx.shape[2] == 3:
return xx[:, 32:32 + 192, :]
with torch.no_grad():
for i in range(num_iterations):
# if i == 1: break
try:
data, model_inputs = next(test_iterator)
except:
break
inpaint_region = data["inpaint_region"]
person_cloth = data["person_cloth"]
cloth_no = int(data['clothno'].numpy()[0])
person_no = int(data['personno'].numpy()[0])
data = dpp(person_cloth, inpaint_region)
model.set_input(data)
I_g, I_o, I_i, val_loss_G, I_raw, L_o, mask = model.optimize_parameters(
val=True)
plt.figure(figsize=(12, 10))
plt.subplot(2, 3, 1)
plt.imshow(tensor2array(I_i))
plt.title("Input", fontsize=20)
plt.subplot(2, 3, 2)
plt.imshow(tensor2array(mask))
plt.title("Inpaint Region (M)", fontsize=20)
plt.subplot(2, 3, 3)
plt.imshow(tensor2array(I_g))
plt.title("Ground Truth (GT)", fontsize=20)
plt.subplot(2, 3, 4)
plt.imshow(tensor2array(L_o))
plt.title("LBP Output", fontsize=20)
plt.subplot(2, 3, 5)
plt.imshow(tensor2array(I_raw))
plt.title("Generator Output (GO)", fontsize=20)
plt.subplot(2, 3, 6)
plt.imshow(tensor2array(I_o))
plt.title("GO*M+GT*(1-M)", fontsize=20)
plt.savefig(f"{result_dir}/{i}_result.jpg")
plt.imsave(f"{result_dir}/{i}_input.jpg", tensor2array(I_i))
plt.imsave(f"{result_dir}/{i}_mask.png", tensor2array(mask))
plt.imsave(f"{result_dir}/{i}_output.jpg", tensor2array(I_o))
val_s, val_p, val_m = metrics(I_g, I_o)
val_ssim.append(val_s)
val_psnr.append(val_p)
val_mae.append(val_m)
val_losses_G.append(val_loss_G.detach().item())
ids.append(str(cloth_no) + '_' + str(person_no))
losses = {
'ssim': val_ssim,
'val_mae': val_mae,
'psnr': val_psnr,
'loss_G': val_losses_G,
'ids': ids
}
import pandas as pd
csv = pd.DataFrame(losses)
csv.to_csv(f"{result_dir}/losses.csv")
cmd = f"gsutil -m cp -r {result_dir} gs://vinit_helper/cloth_inpainting_gan/cloth_inpainting_local_binary_pattern/{opt.checkpoints_dir.split('/')[1]}"
os.system(cmd)
